FRO vs GSL

Frontline Plc vs Global Ship Lease Inc New — Valuation Comparison 2026

FRO

Deep Sea Foreign Transportation of Freight
Frontline Plc
Quality
2.4
out of 10
Value Trap
Price
$34.67
Last close
Models
12/13
Active
VS

GSL

Deep Sea Foreign Transportation of Freight
Global Ship Lease Inc New
Quality
10.0
out of 10
Value Trap
12
SAFE
Price
$36.43
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType FRO Fair ValueFRO Upside GSL Fair ValueGSL Upside
Bayesian DCF Intrinsic $11.65 -66.4%
Earnings Power Value Intrinsic $131.68 +261.5%
EROIC Spread Intrinsic $3.87 -89.7% $69.79 +91.6%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for FRO vs GSL — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FRO vs GSL — Which Stock Is More Undervalued?

GSL scores higher with a 10.0/10 quality rating vs FRO's 2.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Frontline Plc (FRO) and Global Ship Lease Inc New (GSL) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

FRO currently trades at $34.67 with a QOC of 2.4/10, while GSL trades at $36.43 with a QOC of 10.0/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).